Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	140,039
2	Rhode Island	138,124
3	South Dakota	137,890
4	Utah	123,362
5	Tennessee	121,050
6	Arizona	117,979
7	Iowa	115,028
8	Nebraska	113,074
9	Oklahoma	112,851
10	Wisconsin	112,782
11	New Jersey	112,347
12	South Carolina	111,675
13	Arkansas	110,910
14	Alabama	107,303
15	Indiana	106,664
16	Kansas	106,251
17	Delaware	106,003
18	Illinois	104,531
19	New York	104,315
20	Mississippi	104,278
21	Idaho	104,249
22	Florida	102,830
23	Nevada	101,566
24	Montana	101,209
25	Minnesota	100,772
26	Georgia	100,730
27	Wyoming	99,689
28	Kentucky	99,427
29	Texas	99,129
30	Massachusetts	99,061
31	Louisiana	97,991
32	Missouri	97,361
33	California	94,443
34	Connecticut	93,895
35	New Mexico	93,579
36	North Carolina	91,902
37	Michigan	91,234
38	Ohio	91,051
39	Alaska	90,928
40	Pennsylvania	88,933
41	Colorado	87,605
42	West Virginia	84,469
43	Virginia	76,645
44	Maryland	73,348
45	New Hampshire	69,000
46	District of Columbia	67,053
47	Washington	52,135
48	Puerto Rico	50,748
49	Maine	44,469
50	Oregon	42,842
51	Vermont	36,180
52	Hawaii	22,801

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	357
2	Minnesota	344
3	Puerto Rico	338
4	Delaware	326
5	Colorado	288
6	New Jersey	274
7	Florida	272
8	Pennsylvania	270
9	Maine	225
10	New Hampshire	223
11	New York	223
12	Illinois	213
13	Massachusetts	212
14	Oregon	204
15	West Virginia	203
16	Maryland	180
17	South Carolina	177
18	Washington	152
19	Indiana	148
20	Nebraska	142
21	Vermont	134
22	District of Columbia	133
23	North Dakota	132
24	Georgia	131
25	South Dakota	126
26	Virginia	126
27	Ohio	119
28	Utah	114
29	Wisconsin	113
30	Iowa	109
31	Arizona	104
32	Missouri	104
33	Montana	104
34	Connecticut	96
35	Kentucky	96
36	Rhode Island	93
37	North Carolina	76
38	Alabama	73
39	Hawaii	72
40	Texas	70
41	Tennessee	68
42	Alaska	67
43	Nevada	60
44	Kansas	54
45	Arkansas	53
46	Wyoming	47
47	Idaho	40
48	Louisiana	38
49	New Mexico	36
50	California	33
51	Oklahoma	25
52	Mississippi	23

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,857
2	New York	2,645
3	Massachusetts	2,546
4	Rhode Island	2,510
5	Mississippi	2,410
6	Arizona	2,372
7	Connecticut	2,257
8	Louisiana	2,223
9	Alabama	2,213
10	South Dakota	2,213
11	Pennsylvania	2,033
12	North Dakota	1,990
13	Indiana	1,971
14	New Mexico	1,920
15	Illinois	1,904
16	Arkansas	1,894
17	Iowa	1,878
18	Michigan	1,842
19	South Carolina	1,831
20	Georgia	1,823
21	Tennessee	1,763
22	Nevada	1,760
23	Texas	1,727
24	Kansas	1,719
25	Oklahoma	1,697
26	Delaware	1,659
27	Ohio	1,635
28	Florida	1,622
29	West Virginia	1,574
30	District of Columbia	1,557
31	California	1,554
32	Missouri	1,495
33	Kentucky	1,467
34	Montana	1,462
35	Maryland	1,432
36	Wisconsin	1,283
37	Minnesota	1,269
38	Virginia	1,252
39	Nebraska	1,218
40	Wyoming	1,218
41	North Carolina	1,196
42	Idaho	1,137
43	Colorado	1,109
44	New Hampshire	944
45	Washington	719
46	Puerto Rico	708
47	Utah	680
48	Oregon	592
49	Maine	574
50	Alaska	434
51	Vermont	391
52	Hawaii	335

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Michigan	5
2	Delaware	4
3	Kentucky	4
4	Georgia	3
5	New Jersey	3
6	Arizona	2
7	Florida	2
8	Illinois	2
9	Iowa	2
10	Nevada	2
11	New Hampshire	2
12	New York	2
13	Ohio	2
14	Pennsylvania	2
15	Puerto Rico	2
16	South Carolina	2
17	West Virginia	2
18	Wisconsin	2
19	Alabama	1
20	California	1
21	Colorado	1
22	Indiana	1
23	Maryland	1
24	Massachusetts	1
25	Minnesota	1
26	Montana	1
27	Nebraska	1
28	Oregon	1
29	Texas	1
30	Virginia	1
31	Alaska	0
32	Arkansas	0
33	Connecticut	0
34	District of Columbia	0
35	Hawaii	0
36	Idaho	0
37	Kansas	0
38	Louisiana	0
39	Maine	0
40	Mississippi	0
41	Missouri	0
42	New Mexico	0
43	North Carolina	0
44	North Dakota	0
45	Oklahoma	0
46	Rhode Island	0
47	South Dakota	0
48	Tennessee	0
49	Utah	0
50	Vermont	0
51	Washington	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	356,743	1	99
Crowley	Colorado	356,377	2	99
Bent	Colorado	270,038	3	99
Dewey	South Dakota	247,794	4	99
Lincoln	Arkansas	245,700	5	99
Davidson	Tennessee	140,673	185	94
Richland	South Carolina	109,879	997	68
York	South Carolina	107,940	1068	66
Orange	California	84,905	2154	31
Pierce	Washington	52,727	2887	8

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,035	2	99
Jerauld	South Dakota	7,948	3	99
Emporia city	Virginia	7,856	4	99
Hancock	Georgia	7,686	5	99
Orange	California	1,553	1823	41
Davidson	Tennessee	1,338	2072	34
York	South Carolina	1,324	2092	33
Richland	South Carolina	1,301	2114	32
Pierce	Washington	703	2751	12

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.